
"""simple docstring"""


def    _snake_case					(		snake_case__ :  List[str]						,   snake_case__ :  Optional[int]  ):
	# "extended trapezoidal rule"
	# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
	A     = (boundary[1] - boundary[0]) / steps
	A     = boundary[0]
	A     = boundary[1]
	A     = make_points(snake_case__						,   snake_case__						,   snake_case__  )
	A     = 0.0
	y += (h / 2.0) * f(snake_case__  )
	for i in x_i:
		# print(i)
		y += h * f(snake_case__  )
	y += (h / 2.0) * f(snake_case__  )
	return y






def    _snake_case					(		snake_case__ :  Any						,   snake_case__ :  List[Any]						,   snake_case__ :  Any  ):
	A     = a + h
	while x < (b - h):
		yield x
		A     = x + h






def    _snake_case					(		snake_case__ :  Optional[Any]  ):  # enter your function here
	A     = (x - 0) * (x - 0)
	return y






def    _snake_case					(		):
	A     = 0.0  # Lower bound of integration
	A     = 1.0  # Upper bound of integration
	A     = 10.0  # define number of steps or resolution
	A     = [a, b]  # define boundary of integration
	A     = method_a(snake_case__						,   snake_case__  )
	print(F'y = {y}'  )


if __name__ == "__main__":
		main()

from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCAmelCase     :	Union[str, Any]       			=  {
    'configuration_informer': [
        'INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'InformerConfig',
    ],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCAmelCase     :	Optional[Any]       			=  [
        'INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
        'InformerForPrediction',
        'InformerModel',
        'InformerPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_informer import (
            INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            InformerForPrediction,
            InformerModel,
            InformerPreTrainedModel,
        )

else:
    import sys

    __lowerCAmelCase     :	List[str]       			=  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

